AWS Bedrock Embedder

The AwsBedrockEmbedder class is used to embed text data into vectors using the AWS Bedrock API. By default, it uses the Cohere Embed Multilingual V3 model for generating embeddings.

Setup

Set your AWS credentials

export AWS_ACCESS_KEY_ID = xxx
export AWS_SECRET_ACCESS_KEY = xxx
export AWS_REGION = xxx

By default, this embedder uses the cohere.embed-multilingual-v3 model. You must enable access to this model from the AWS Bedrock model catalog before using this embedder.

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:16

Usage

aws_bedrock_embedder.py

import asyncio
from agno.knowledge.embedder.aws_bedrock import AwsBedrockEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.pgvector import PgVector

embeddings = AwsBedrockEmbedder().get_embedding(
    "The quick brown fox jumps over the lazy dog."
)
# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Example usage:
knowledge = Knowledge(
    vector_db=PgVector(
        table_name="recipes",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        embedder=AwsBedrockEmbedder(),
    ),
)

knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(
        chunk_size=2048
    ),  # Required because cohere has a fixed size of 2048
)

Params

Parameter	Type	Default	Description
id	str	"cohere.embed-multilingual-v3"	The model ID to use. You need to enable this model in your AWS Bedrock model catalog.
dimensions	int	1024	The dimensionality of the embeddings generated by the model(1024 for Cohere models).
input_type	str	"search_query"	Prepends special tokens to differentiate types. Options: ‘search_document’, ‘search_query’, ‘classification’, ‘clustering’.
truncate	Optional[str]	None	How to handle inputs longer than the maximum token length. Options: ‘NONE’, ‘START’, ‘END’.
embedding_types	Optional[List[str]]	None	Types of embeddings to return . Options: ‘float’, ‘int8’, ‘uint8’, ‘binary’, ‘ubinary’.
aws_region	Optional[str]	None	The AWS region to use. If not provided, falls back to AWS_REGION env variable.
aws_access_key_id	Optional[str]	None	The AWS access key ID. If not provided, falls back to AWS_ACCESS_KEY_ID env variable.
aws_secret_access_key	Optional[str]	None	The AWS secret access key. If not provided, falls back to AWS_SECRET_ACCESS_KEY env variable.
session	Optional[Session]	None	A boto3 Session object to use for authentication.
request_params	Optional[Dict[str, Any]]	None	Additional parameters to pass to the API requests.
client_params	Optional[Dict[str, Any]]	None	Additional parameters to pass to the boto3 client.
client	Optional[AwsClient]	None	An instance of the AWS Bedrock client to use for making API requests.

Developer Resources

• View Cookbook